More recently, joint detection and tracking based methods have drawn great attention [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. Usually, such trackers take sequential adjacent frames as input. Features are aggregated in different frames, and bounding box regression is conducted with temporal information. For example, Tracktor [5]} applies bounding box regression to refine the box position in the next frame to form the track; JDE [6]} incorporates the appearance embedding model into a single-shot detector that can simultaneously
output detections and the corresponding embeddings; while CenterTrack [1]} applies a detection model to a pair of images and detections from the prior frame. Besides that, CTracker [2]} constructs tracklets by chaining paired boxes in every two frames. TubeTK [4]} directly predicts a box tube as a tracklet in an offline manner. [7]} proposes a quasi-dense similarity learning approach that densely samples hundreds of region proposals on a pair of images for contrastive learning. However, due to the heavy computational cost, the networks can only take a very limited number of frames as input. However, such methods usually suffer long-time occlusions, which further results in temporal association error.
